A Support Vector Machine Approach for Video Shot Detection

Abstract

The first step towards indexing and content based video retrieval is video shot detection. Existing methodologies for video shot detection are mostly threshold dependent. No prior knowledge about the video content makes such methods sensitive to video content. To ameliorate this shortcoming we propose a learning based methodology using a set of features that are specifically designed to capture the differences among hard cuts, gradual transitions and normal sequences of frames simultaneously. A Support Vector Machine (SVM) classifier is trained both to locate shot boundaries and characterize transition types. Numerical experiments using a variety of videos demonstrate that our method is capable of accurately detecting and discriminating shot transitions in videos with different characteristics.